interfaces.dipy.tracks¶
StreamlineTractography¶
Streamline tractography using EuDX [Garyfallidis12].
[Garyfallidis12] | Garyfallidis E., “Towards an accurate brain tractography”, PhD thesis, University of Cambridge, 2012 |
Example¶
>>> from nipype.interfaces import dipy as ndp
>>> track = ndp.StreamlineTractography()
>>> track.inputs.in_file = '4d_dwi.nii'
>>> track.inputs.in_model = 'model.pklz'
>>> track.inputs.tracking_mask = 'dilated_wm_mask.nii'
>>> res = track.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
input diffusion data
gfa_thresh: (a float, nipype default value: 0.2)
GFA threshold to compute tracking mask
peak_threshold: (a float, nipype default value: 0.5)
threshold to consider peaks from model
min_angle: (a float, nipype default value: 25.0)
minimum separation angle
multiprocess: (a boolean, nipype default value: True)
use multiprocessing
save_seeds: (a boolean, nipype default value: False)
save seeding voxels coordinates
num_seeds: (an integer (int or long), nipype default value: 10000)
desired number of tracks in tractography
[Optional]
in_model: (a pathlike object or string representing an existing file)
input f/d-ODF model extracted from.
tracking_mask: (a pathlike object or string representing an existing
file)
input mask within which perform tracking
seed_mask: (a pathlike object or string representing an existing
file)
input mask within which perform seeding
in_peaks: (a pathlike object or string representing an existing file)
peaks computed from the odf
seed_coord: (a pathlike object or string representing an existing
file)
file containing the list of seed voxel coordinates (N,3)
out_prefix: (a unicode string)
output prefix for file names
Outputs:
tracks: (a pathlike object or string representing a file)
TrackVis file containing extracted streamlines
gfa: (a pathlike object or string representing a file)
The resulting GFA (generalized FA) computed using the peaks of the
ODF
odf_peaks: (a pathlike object or string representing a file)
peaks computed from the odf
out_seeds: (a pathlike object or string representing a file)
file containing the (N,3) *voxel* coordinates used in seeding.
TrackDensityMap¶
Creates a tract density image from a TrackVis track file using functions from dipy
Example¶
>>> import nipype.interfaces.dipy as dipy
>>> trk2tdi = dipy.TrackDensityMap()
>>> trk2tdi.inputs.in_file = 'converted.trk'
>>> trk2tdi.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
The input TrackVis track file
[Optional]
reference: (a pathlike object or string representing an existing
file)
A reference file to define RAS coordinates space
points_space: ('rasmm' or 'voxel' or None, nipype default value:
rasmm)
coordinates of trk file
voxel_dims: (a list of from 3 to 3 items which are a float)
The size of each voxel in mm.
data_dims: (a list of from 3 to 3 items which are an integer (int or
long))
The size of the image in voxels.
out_filename: (a pathlike object or string representing a file,
nipype default value: tdi.nii)
The output filename for the tracks in TrackVis (.trk) format
Outputs:
out_file: (a pathlike object or string representing an existing file)